Categories: AI/ML Research

Decision Trees and Ordinal Encoding: A Practical Guide

Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and […]

The post Decision Trees and Ordinal Encoding: A Practical Guide appeared first on MachineLearningMastery.com.

One Hot Encoding: Understanding the “Hot” in Data

Preparing categorical data correctly is a fundamental step in machine learning, particularly when using linear models. One Hot Encoding stands out as a key technique, enabling the transformation of categorical variables into a machine-understandable format. This post tells you why you cannot use a categorical variable directly and demonstrates the…

August 19, 2024

In "AI/ML Research"

Refit trained parameters on large datasets using Amazon SageMaker Data Wrangler

November 15, 2022

In "FAANG"

Sparse Features Support in BigQuery

IntroductionMost machine learning models require the input features to be in numerical format and if the features are in categorial format, pre-processing steps such as one-hot encoding are needed to convert them into numerical format. Converting a large number of categorical values may lead to creating sparse features, a set…

January 11, 2023

In "FAANG"

AI Generated Robotic Content